Install packages

if(!require("htmltools")) install.packages("htmltools") 
if(!require("remotes")) install.packages("remotes") 
if(!require("MultiEWCE")) remotes::install_github("neurogenomics/MutltiEWCE", dependencies = TRUE)

Prioritise targets

Filter & sort

res <- MultiEWCE::prioritise_targets()
## Prioritising gene targets.
## Importing existing file: ... phenotype_to_genes.txt
## Adding term definitions.
## Adding level-3 ancestor to each HPO ID.
## Prioritised targets: step='start' 
##  - rows: 435,204 
##  - phenotypes: 5,652 
##  - celltypes: 77
## Filtering @ q-value <= 0.05
## Prioritised targets: step='q_threshold' 
##  - rows: 7,648 
##  - phenotypes: 2,580 
##  - celltypes: 77
## Filtering @ fold-change >= 1
## Prioritised targets: step='fold_threshold' 
##  - rows: 7,648 
##  - phenotypes: 2,580 
##  - celltypes: 77
## Getting absolute ontology level for 2,580 HPO IDs.
## Prioritised targets: step='keep_ont_levels' 
##  - rows: 7,648 
##  - phenotypes: 2,580 
##  - celltypes: 77
## Annotating phenos with Onset.
## Importing existing file: ... phenotype.hpoa
## Importing existing file: ... phenotype.hpoa
## Prioritised targets: step='keep_onsets' 
##  - rows: 7,489 
##  - phenotypes: 2,520 
##  - celltypes: 77
## Annotating phenos with Tiers.
## Prioritised targets: step='keep_tiers' 
##  - rows: 722 
##  - phenotypes: 132 
##  - celltypes: 66
## Annotating phenos with Modifiers
## Prioritised targets: step='severity_threshold' 
##  - rows: 677 
##  - phenotypes: 127 
##  - diseases: 15 
##  - celltypes: 66
## Annotating phenotype frequencies.
## Prioritised targets: step='pheno_frequency_threshold' 
##  - rows: 577 
##  - phenotypes: 104 
##  - diseases: 14 
##  - celltypes: 64
## 33 / 64 of cell types kept.
## Prioritised targets: step='keep_celltypes' 
##  - rows: 363 
##  - phenotypes: 89 
##  - diseases: 14 
##  - celltypes: 33
## Filtering by gene size.
## Converting phenos to GRanges.
## Importing existing file: ... phenotype_to_genes.txt
## Loading required namespace: ensembldb
## Gathering gene metadata
## Loading required namespace: EnsDb.Hsapiens.v75
## Prioritised targets: step='keep_seqnames' 
##  - rows: 54,337 
##  - phenotypes: 89 
##  - genes: 3,603
## 218 / 3,603 genes kept.
## Prioritised targets: step='gene_size' 
##  - rows: 2,779 
##  - phenotypes: 87 
##  - genes: 218
## Prioritised targets: step='keep_biotypes' 
##  - rows: 2,779 
##  - phenotypes: 87 
##  - genes: 218
## Filtering by specificity_quantile.
## Filtering by mean_exp_quantile.
## Annotating gene frequencies.
## Importing existing file: ... genes_to_phenotype.txt
## Prioritised targets: step='gene_frequency_threshold' 
##  - rows: 11,392 
##  - phenotypes: 85 
##  - diseases: 13 
##  - celltypes: 32 
##  - genes: 217
## Prioritised targets: step='keep_specificity_quantiles' 
##  - rows: 309 
##  - phenotypes: 61 
##  - diseases: 12 
##  - celltypes: 27 
##  - genes: 62
## Prioritised targets: step='keep_mean_exp_quantiles' 
##  - rows: 309 
##  - phenotypes: 61 
##  - diseases: 12 
##  - celltypes: 27 
##  - genes: 62
## Sorting rows.
## Finding top 20 gene targets per: HPO_ID, CellType
## Prioritised targets: step='top_n' 
##  - rows: 309 
##  - phenotypes: 61 
##  - diseases: 12 
##  - celltypes: 27 
##  - genes: 62
## Prioritised targets: step='end' 
##  - rows: 309 
##  - phenotypes: 61 
##  - diseases: 12 
##  - celltypes: 27 
##  - genes: 62

Top targets

Here are the top gene targets based on the default filtering/sorting criterion of prioritise_targets.

MultiEWCE::create_dt(res$top_targets)
## Loading required namespace: DT

Filtering report

Here is a report that shows how many phenotypes/celltypes/genes remained after each step within the prioritise_targets pipeline.

MultiEWCE::create_dt(res$report)

Plot network

Generate a network from the top phenotype-celltype-gene associations.

vn_top <- MultiEWCE::prioritise_targets_network(
    top_targets = res$top_targets, 
    save_path = here::here("reports","top_targets_network.html"), 
    show_plot = FALSE)
## Loading required namespace: visNetwork
## Loading required namespace: igraph
## Creating network.
## Creating plot.
## Saving plot ==> /var/folders/zq/h7mtybc533b1qzkys_ttgpth0000gn/T//RtmpoCEivZ/filea3a4f9d2369_prioritise_targets_network.html
# visNetwork::visExport(vn_top$plot, type = "pdf", loadDependencies = T)
# visNetwork::renderVisNetwork(vn_top$plot)
pagedown::chrome_print(input = here::here("reports","top_targets_network.html"),
                       output = here::here("reports","top_targets_network.pdf"),
                       format = "pdf")
htmltools::includeHTML("https://github.com/neurogenomics/RareDiseasePrioritisation/raw/master/reports/top_targets_network.html")
visNetwork

Aggregate results

df_agg <- MultiEWCE::agg_results(
    phenos = res$top_targets,
    count_var = "CellType", 
    group_var = c("Phenotype","ontLvl",
                  "tier","tier_auto",
                  "ancestor","ancestor_name",
                  "disease_characteristic","DiseaseNames",
                  "Onset","Onset_earliest","Onset_score_mean","Onset_score_min",
                  "pheno_freq_mean","pheno_freq_min",
                  "Severity_score_mean","Severity_score_min")
    )
## Aggregating results by group_var='Phenotype'Aggregating results by group_var='ontLvl'Aggregating results by group_var='tier'Aggregating results by group_var='tier_auto'Aggregating results by group_var='ancestor'Aggregating results by group_var='ancestor_name'Aggregating results by group_var='disease_characteristic'Aggregating results by group_var='DiseaseNames'Aggregating results by group_var='Onset'Aggregating results by group_var='Onset_earliest'Aggregating results by group_var='Onset_score_mean'Aggregating results by group_var='Onset_score_min'Aggregating results by group_var='pheno_freq_mean'Aggregating results by group_var='pheno_freq_min'Aggregating results by group_var='Severity_score_mean'Aggregating results by group_var='Severity_score_min'
## Adding HPO IDs.
## Importing existing file: ... phenotype_to_genes.txt
MultiEWCE::create_dt(df_agg)

Aggregate results

Subset phenotypes to those included in intellectual disability, and are related to cognition.

df_intel <- HPOExplorer::add_ancestor(
    phenos = res$top_targets[ancestor_name=="Abnormality of the nervous system",][,-c("ancestor","ancestor_name")],
                                      lvl = 5)
## Adding level-5 ancestor to each HPO ID.
df_intel <- df_intel[
    ancestor_name %in% c("Neurodevelopmental abnormality"),]
sort(unique(df_intel$Phenotype))
## [1] "Developmental regression"          "Global developmental delay"       
## [3] "Intellectual disability"           "Intellectual disability, mild"    
## [5] "Intellectual disability, moderate" "Intellectual disability, severe"  
## [7] "Neurodevelopmental abnormality"    "Neurodevelopmental delay"         
## [9] "Severe global developmental delay"

Top genes

top_genes <- sort(table(df_intel$Gene),
                  decreasing = TRUE)
print(top_genes)
## 
##       SOX3       SOX2     POU3F4      FOXH1       RTL1      FOXG1       GSX2 
##         16         12          9          8          7          6          6 
##       PIGY     TUBB2A    SLC18A3      ASCL1      GPR88      HOXA2    MAB21L2 
##          6          6          5          4          4          4          4 
##      PROP1       SIX6       HES7 SNORD116-1   SNORD118       ROM1        TRH 
##          4          4          3          3          3          2          2 
##       HPDL       JAG1      KLRC4      PRRT2    RIPPLY2      RNU12 
##          1          1          1          1          1          1

Top cell types

top_celltypes <- sort(table(unique(df_intel[,c("Phenotype","HPO_ID","CellType")])$CellType),
         decreasing = TRUE)
print(top_celltypes)
## 
##  Excitatory neurons     Granule neurons  Inhibitory neurons          Astrocytes 
##                   6                   6                   5                   4 
##            ENS glia      Ganglion cells    Horizontal cells    Oligodendrocytes 
##                   4                   4                   4                   4 
##    Purkinje neurons    Visceral neurons      Amacrine cells         ENS neurons 
##                   4                   3                   2                   2 
##       Bipolar cells Photoreceptor cells       Schwann cells 
##                   1                   1                   1

Prioritise targets: extended

Now let’s lift some of the filters on phenotypes and cell types to recover a more extensive network.

Filter & sort

res_all <- MultiEWCE::prioritise_targets(keep_onsets = NULL,
                                         keep_tiers = NULL,
                                         severity_threshold = NULL,
                                         pheno_frequency_threshold = c(10,NA),
                                         gene_frequency_threshold = c(10,NA),
                                         keep_specificity_quantiles = seq(20,40),
                                         keep_mean_exp_quantiles = seq(20,40))
## Prioritising gene targets.
## Importing existing file: ... phenotype_to_genes.txt
## Adding term definitions.
## Adding level-3 ancestor to each HPO ID.
## Prioritised targets: step='start' 
##  - rows: 435,204 
##  - phenotypes: 5,652 
##  - celltypes: 77
## Filtering @ q-value <= 0.05
## Prioritised targets: step='q_threshold' 
##  - rows: 7,648 
##  - phenotypes: 2,580 
##  - celltypes: 77
## Filtering @ fold-change >= 1
## Prioritised targets: step='fold_threshold' 
##  - rows: 7,648 
##  - phenotypes: 2,580 
##  - celltypes: 77
## Getting absolute ontology level for 2,580 HPO IDs.
## Prioritised targets: step='keep_ont_levels' 
##  - rows: 7,648 
##  - phenotypes: 2,580 
##  - celltypes: 77
## Annotating phenos with Onset.
## Importing existing file: ... phenotype.hpoa
## Prioritised targets: step='keep_onsets' 
##  - rows: 7,648 
##  - phenotypes: 2,580 
##  - celltypes: 77
## Annotating phenos with Tiers.
## Prioritised targets: step='keep_tiers' 
##  - rows: 7,656 
##  - phenotypes: 2,580 
##  - celltypes: 77
## Annotating phenos with Modifiers
## Prioritised targets: step='severity_threshold' 
##  - rows: 7,656 
##  - phenotypes: 2,580 
##  - diseases: 225 
##  - celltypes: 77
## Annotating phenotype frequencies.
## Prioritised targets: step='pheno_frequency_threshold' 
##  - rows: 7,574 
##  - phenotypes: 2,543 
##  - diseases: 225 
##  - celltypes: 76
## 37 / 76 of cell types kept.
## Prioritised targets: step='keep_celltypes' 
##  - rows: 3,855 
##  - phenotypes: 1,711 
##  - diseases: 177 
##  - celltypes: 37
## Filtering by gene size.
## Converting phenos to GRanges.
## Importing existing file: ... phenotype_to_genes.txt
## Gathering gene metadata
## Prioritised targets: step='keep_seqnames' 
##  - rows: 665,375 
##  - phenotypes: 1,711 
##  - genes: 4,329
## 289 / 4,329 genes kept.
## Prioritised targets: step='gene_size' 
##  - rows: 38,512 
##  - phenotypes: 1,485 
##  - genes: 289
## Prioritised targets: step='keep_biotypes' 
##  - rows: 38,512 
##  - phenotypes: 1,485 
##  - genes: 289
## Filtering by specificity_quantile.
## Filtering by mean_exp_quantile.
## Annotating gene frequencies.
## Importing existing file: ... genes_to_phenotype.txt
## Prioritised targets: step='gene_frequency_threshold' 
##  - rows: 114,312 
##  - phenotypes: 1,454 
##  - diseases: 170 
##  - celltypes: 37 
##  - genes: 289
## Prioritised targets: step='keep_specificity_quantiles' 
##  - rows: 28,299 
##  - phenotypes: 1,293 
##  - diseases: 160 
##  - celltypes: 37 
##  - genes: 250
## Prioritised targets: step='keep_mean_exp_quantiles' 
##  - rows: 28,299 
##  - phenotypes: 1,293 
##  - diseases: 160 
##  - celltypes: 37 
##  - genes: 250
## Sorting rows.
## Finding top 20 gene targets per: HPO_ID, CellType
## Prioritised targets: step='top_n' 
##  - rows: 23,293 
##  - phenotypes: 1,293 
##  - diseases: 160 
##  - celltypes: 37 
##  - genes: 250
## Prioritised targets: step='end' 
##  - rows: 23,293 
##  - phenotypes: 1,293 
##  - diseases: 160 
##  - celltypes: 37 
##  - genes: 250

Plot network

Note that we set physics=FALSE to reduce the computation that it takes to render the plot.

vn_all <- MultiEWCE::prioritise_targets_network(top_targets = res_all$top_targets, 
                                                save_path = here::here("reports",
                                                            "all_targets_network.html"), 
                                                
                                                physics = FALSE,  
                                                show_plot = FALSE)
## Creating network.
## Creating plot.
## Saving plot ==> /var/folders/zq/h7mtybc533b1qzkys_ttgpth0000gn/T//RtmpoCEivZ/filea3a44ada4635_prioritise_targets_network.html
htmltools::includeHTML("https://github.com/neurogenomics/RareDiseasePrioritisation/raw/master/reports/all_targets_network.html")
visNetwork

Aggregate results

all_agg <- MultiEWCE::agg_results(phenos = res_all$top_targets,
                                  count_var = "CellType", 
                                  group_var = "Phenotype")
## Aggregating results by group_var='Phenotype'
## Adding HPO IDs.
## Importing existing file: ... phenotype_to_genes.txt
MultiEWCE::create_dt(all_agg) 

Top phenotypes

Get the phenotypes that were enriched in the greatest number of cell types.

#### All ontology level ####
head(sort(table(unique(res_all$top_targets[,c("Phenotype","CellType")])$Phenotype), 
          decreasing = TRUE))
## 
##        Abnormality of eye movement            Intellectual disability 
##                                 16                                 16 
##         Abnormal muscle physiology               Abnormal muscle tone 
##                                 15                                 15 
## Abnormal nervous system physiology     Abnormality of the musculature 
##                                 15                                 15
#### Only lower ontology levels #####
all_targets <- HPOExplorer::add_ont_lvl(res_all$top_targets)
head(sort(table(unique(res_all$top_targets[ontLvl<=4,c("Phenotype","CellType")])$Phenotype), 
          decreasing = TRUE))
## 
##            Intellectual disability                          Hypotonia 
##                                 16                                 15 
##     Neurodevelopmental abnormality Abnormal involuntary eye movements 
##                                 15                                 14 
##     Neurological speech impairment                          Nystagmus 
##                                 14                                 14

Top cell types

Get the cell types that were enriched in the greatest number of unique phenotypes.

head(sort(table(unique(res_all$top_targets[,c("Phenotype","CellType")])$CellType), 
          decreasing = TRUE))
## 
##       Excitatory neurons Antigen presenting cells    Limbic system neurons 
##                      248                      193                      192 
##           Ganglion cells           Cardiomyocytes                 ENS glia 
##                      169                      167                      167

Top genes

Get the genes that were enriched in the greatest number of unique phenotypes.

head(sort(table(unique(res_all$top_targets[,c("Phenotype","Gene")])$Gene), 
          decreasing = TRUE))
## 
## RNU4ATAC     RMRP   MAGEL2  B3GALT6  TMEM107    TREX1 
##      198      197      194      190      184      178

Top ancestors

Get the most common ancestors in the results.

ancestor_freq <- sort(table(res_all$top_targets$ancestor), decreasing = TRUE) |>
    data.table::data.table() |> 
    `colnames<-`(c("HPO_ID","freq"))
ancestor_freq$Phenotype <- HPOExplorer::harmonise_phenotypes(phenotypes = ancestor_freq$HPO_ID)

MultiEWCE::create_dt(ancestor_freq) 

Manual queries

Get all results.

res_all <- MultiEWCE::prioritise_targets(keep_onsets = NULL,
                                         keep_tiers = NULL,
                                         severity_threshold = NULL,
                                         pheno_frequency_threshold = NULL,
                                         gene_frequency_threshold = NULL,
                                         keep_specificity_quantiles = NULL,
                                         keep_mean_exp_quantiles = NULL)
## Prioritising gene targets.
## Importing existing file: ... phenotype_to_genes.txt
## Adding term definitions.
## Adding level-3 ancestor to each HPO ID.
## Prioritised targets: step='start' 
##  - rows: 435,204 
##  - phenotypes: 5,652 
##  - celltypes: 77
## Filtering @ q-value <= 0.05
## Prioritised targets: step='q_threshold' 
##  - rows: 7,648 
##  - phenotypes: 2,580 
##  - celltypes: 77
## Filtering @ fold-change >= 1
## Prioritised targets: step='fold_threshold' 
##  - rows: 7,648 
##  - phenotypes: 2,580 
##  - celltypes: 77
## Getting absolute ontology level for 2,580 HPO IDs.
## Prioritised targets: step='keep_ont_levels' 
##  - rows: 7,648 
##  - phenotypes: 2,580 
##  - celltypes: 77
## Annotating phenos with Onset.
## Importing existing file: ... phenotype.hpoa
## Prioritised targets: step='keep_onsets' 
##  - rows: 7,648 
##  - phenotypes: 2,580 
##  - celltypes: 77
## Annotating phenos with Tiers.
## Prioritised targets: step='keep_tiers' 
##  - rows: 7,656 
##  - phenotypes: 2,580 
##  - celltypes: 77
## Annotating phenos with Modifiers
## Prioritised targets: step='severity_threshold' 
##  - rows: 7,656 
##  - phenotypes: 2,580 
##  - diseases: 225 
##  - celltypes: 77
## Annotating phenotype frequencies.
## Prioritised targets: step='pheno_frequency_threshold' 
##  - rows: 7,656 
##  - phenotypes: 2,580 
##  - diseases: 225 
##  - celltypes: 77
## 37 / 77 of cell types kept.
## Prioritised targets: step='keep_celltypes' 
##  - rows: 3,888 
##  - phenotypes: 1,733 
##  - diseases: 177 
##  - celltypes: 37
## Filtering by gene size.
## Converting phenos to GRanges.
## Importing existing file: ... phenotype_to_genes.txt
## Gathering gene metadata
## Prioritised targets: step='keep_seqnames' 
##  - rows: 679,916 
##  - phenotypes: 1,733 
##  - genes: 4,329
## 289 / 4,329 genes kept.
## Prioritised targets: step='gene_size' 
##  - rows: 39,405 
##  - phenotypes: 1,503 
##  - genes: 289
## Prioritised targets: step='keep_biotypes' 
##  - rows: 39,405 
##  - phenotypes: 1,503 
##  - genes: 289
## Annotating gene frequencies.
## Importing existing file: ... genes_to_phenotype.txt
## Prioritised targets: step='gene_frequency_threshold' 
##  - rows: 120,041 
##  - phenotypes: 1,503 
##  - diseases: 171 
##  - celltypes: 37 
##  - genes: 289
## Prioritised targets: step='keep_specificity_quantiles' 
##  - rows: 120,041 
##  - phenotypes: 1,503 
##  - diseases: 171 
##  - celltypes: 37 
##  - genes: 289
## Prioritised targets: step='keep_mean_exp_quantiles' 
##  - rows: 120,041 
##  - phenotypes: 1,503 
##  - diseases: 171 
##  - celltypes: 37 
##  - genes: 289
## Sorting rows.
## Finding top 20 gene targets per: HPO_ID, CellType
## Prioritised targets: step='top_n' 
##  - rows: 47,285 
##  - phenotypes: 1,503 
##  - diseases: 171 
##  - celltypes: 37 
##  - genes: 285
## Prioritised targets: step='end' 
##  - rows: 47,285 
##  - phenotypes: 1,503 
##  - diseases: 171 
##  - celltypes: 37 
##  - genes: 285

Mental deterioration

Loss of previously present mental abilities, generally in adults. Synonyms: Cognitive decline, Cognitive decline, progressive, Intellectual deterioration, Mental deterioration, Progressive cognitive decline

Not included in prioritse_targets outputs by default because: - “tier_auto=3” - “specificity_quantile” (median=6) and “mean_exp_quantile” (median=6) are quite low for most genes associated with “Mental deterioration”

md_targets <- res_all$top_targets[Phenotype=="Mental deterioration" & q<=0.05] 

sort(table(md_targets[CellType %in% c("Amacrine cells","Ganglion cells")]$Gene), 
     decreasing = TRUE)
## 
## SNORD118     APOE  CHCHD10     CSTB      FTL    GLUD2 HSD17B10    HTRA2 
##        4        2        2        2        2        2        2        2 
##   KCNJ11  NDUFAF3   NHLRC1     SCO2   SDHAF1   TIMM8A    TINF2    TREX1 
##        2        2        2        2        2        2        2        2 
##   TYROBP   UBQLN2 
##        2        2

Recurrent Neisserial infections

results <- MultiEWCE::load_example_results()
## Importing existing file: ... phenotype_to_genes.txt
rci_targets <-results[Phenotype=="Recurrent Neisserial infections" & q<=0.05] 
p2g <- HPOExplorer::load_phenotype_to_genes()
## Importing existing file: ... phenotype_to_genes.txt
data.table::setnames(p2g,"ID","HPO_ID")

rci_targets <- data.table::merge.data.table(rci_targets,
                                            p2g[,c("HPO_ID","Gene")],
                                            by="HPO_ID")
MultiEWCE::create_dt(rci_targets)
MultiEWCE::agg_results(rci_targets)
## Aggregating results by group_var='CellType'
##                  CellType n_phenotype n_genes                           genes
## 1: AFP ALB positive cells           1       7 C7; C5; C6; C8B; CFB; CFI; MBL2
## 2:           Hepatoblasts           1       7 C7; C5; C6; C8B; CFB; CFI; MBL2
##    mean_fold_change mean_q mean_sd_from_mean                       phenotype
## 1:           11.517  0.008             8.388 Recurrent Neisserial infections
## 2:            9.902  0.013             7.449 Recurrent Neisserial infections

Session info

utils::sessionInfo()
## R version 4.2.1 (2022-06-23)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Big Sur ... 10.16
## 
## Matrix products: default
## BLAS:   /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
## [1] MultiEWCE_0.1.3 remotes_2.4.2   htmltools_0.5.4
## 
## loaded via a namespace (and not attached):
##   [1] utf8_1.2.3                    R.utils_2.12.2               
##   [3] tidyselect_1.2.0              RSQLite_2.2.20               
##   [5] AnnotationDbi_1.60.0          htmlwidgets_1.6.1            
##   [7] grid_4.2.1                    BiocParallel_1.32.5          
##   [9] munsell_0.5.0                 codetools_0.2-18             
##  [11] DT_0.27                       colorspace_2.1-0             
##  [13] Biobase_2.58.0                filelock_1.0.2               
##  [15] knitr_1.42                    rstudioapi_0.14              
##  [17] orthogene_1.4.1               stats4_4.2.1                 
##  [19] SingleCellExperiment_1.20.0   ggsignif_0.6.4               
##  [21] MatrixGenerics_1.10.0         GenomeInfoDbData_1.2.9       
##  [23] bit64_4.0.5                   rprojroot_2.0.3              
##  [25] coda_0.19-4                   vctrs_0.5.2                  
##  [27] treeio_1.22.0                 generics_0.1.3               
##  [29] xfun_0.37                     BiocFileCache_2.6.0          
##  [31] pagedown_0.20                 R6_2.5.1                     
##  [33] GenomeInfoDb_1.34.8           pals_1.7                     
##  [35] AnnotationFilter_1.22.0       bitops_1.0-7                 
##  [37] cachem_1.0.6                  gridGraphics_0.5-1           
##  [39] DelayedArray_0.24.0           assertthat_0.2.1             
##  [41] promises_1.2.0.1              BiocIO_1.8.0                 
##  [43] scales_1.2.1                  gtable_0.3.1                 
##  [45] processx_3.8.0                ontologyPlot_1.6             
##  [47] ensembldb_2.22.0              rlang_1.0.6                  
##  [49] rtracklayer_1.58.0            rstatix_0.7.2                
##  [51] lazyeval_0.2.2                dichromat_2.0-0.1            
##  [53] broom_1.0.3                   BiocManager_1.30.19          
##  [55] yaml_2.3.7                    reshape2_1.4.4               
##  [57] HPOExplorer_0.99.3            abind_1.4-5                  
##  [59] GenomicFeatures_1.50.4        ggnetwork_0.5.10             
##  [61] crosstalk_1.2.0               backports_1.4.1              
##  [63] httpuv_1.6.8                  tools_4.2.1                  
##  [65] ggplotify_0.1.0               statnet.common_4.8.0         
##  [67] ggplot2_3.4.0                 ellipsis_0.3.2               
##  [69] jquerylib_0.1.4               paintmap_1.0                 
##  [71] BiocGenerics_0.44.0           servr_0.25                   
##  [73] Rcpp_1.0.10                   plyr_1.8.8                   
##  [75] visNetwork_2.1.2              progress_1.2.2               
##  [77] zlibbioc_1.44.0               purrr_1.0.1                  
##  [79] RCurl_1.98-1.10               ps_1.7.2                     
##  [81] prettyunits_1.1.1             ggpubr_0.5.0                 
##  [83] S4Vectors_0.36.1              SummarizedExperiment_1.28.0  
##  [85] grr_0.9.5                     here_1.0.1                   
##  [87] magrittr_2.0.3                data.table_1.14.6            
##  [89] ProtGenerics_1.30.0           matrixStats_0.63.0           
##  [91] hms_1.1.2                     patchwork_1.1.2              
##  [93] mime_0.12                     evaluate_0.20                
##  [95] xtable_1.8-4                  XML_3.99-0.13                
##  [97] EWCE_1.6.0                    IRanges_2.32.0               
##  [99] compiler_4.2.1                biomaRt_2.54.0               
## [101] tibble_3.1.8                  maps_3.4.1                   
## [103] websocket_1.4.1               crayon_1.5.2                 
## [105] R.oo_1.25.0                   ggfun_0.0.9                  
## [107] later_1.3.0                   tidyr_1.3.0                  
## [109] aplot_0.1.9                   DBI_1.1.3                    
## [111] ExperimentHub_2.6.0           gprofiler2_0.2.1             
## [113] dbplyr_2.3.0                  rappdirs_0.3.3               
## [115] babelgene_22.9                EnsDb.Hsapiens.v75_2.99.0    
## [117] Matrix_1.5-3                  car_3.1-1                    
## [119] piggyback_0.1.4               cli_3.6.0                    
## [121] R.methodsS3_1.8.2             parallel_4.2.1               
## [123] igraph_1.3.5                  GenomicRanges_1.50.2         
## [125] pkgconfig_2.0.3               GenomicAlignments_1.34.0     
## [127] plotly_4.10.1                 xml2_1.3.3                   
## [129] ggtree_3.6.2                  bslib_0.4.2                  
## [131] XVector_0.38.0                yulab.utils_0.0.6            
## [133] stringr_1.5.0                 digest_0.6.31                
## [135] graph_1.76.0                  Biostrings_2.66.0            
## [137] rmarkdown_2.20                HGNChelper_0.8.1             
## [139] tidytree_0.4.2                restfulr_0.0.15              
## [141] curl_5.0.0                    shiny_1.7.4                  
## [143] Rsamtools_2.14.0              rjson_0.2.21                 
## [145] lifecycle_1.0.3               nlme_3.1-161                 
## [147] jsonlite_1.8.4                carData_3.0-5                
## [149] network_1.18.1                mapproj_1.2.11               
## [151] viridisLite_0.4.1             limma_3.54.1                 
## [153] fansi_1.0.4                   pillar_1.8.1                 
## [155] ontologyIndex_2.10            lattice_0.20-45              
## [157] homologene_1.4.68.19.3.27     KEGGREST_1.38.0              
## [159] fastmap_1.1.0                 httr_1.4.4                   
## [161] interactiveDisplayBase_1.36.0 glue_1.6.2                   
## [163] RNOmni_1.0.1                  png_0.1-8                    
## [165] ewceData_1.6.0                BiocVersion_3.16.0           
## [167] bit_4.0.5                     Rgraphviz_2.42.0             
## [169] stringi_1.7.12                sass_0.4.5                   
## [171] blob_1.2.3                    AnnotationHub_3.6.0          
## [173] memoise_2.0.1                 dplyr_1.1.0                  
## [175] ape_5.6-2